{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import xgboost\n",
    "import shap\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, Imputer\n",
    "import sklearn\n",
    "import matplotlib.pyplot as pl\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import keras\n",
    "import pandas as pd\n",
    "import loadnhanes\n",
    "import lifelines\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-21.08333333 -21.08333333 -21.08333333 ... -17.08333333 -17.08333333\n",
      " -17.08333333]\n",
      "number of people surviving  9622\n",
      "number of people not surviving  4785\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/gws/hughchen/anaconda2/envs/py36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:58: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "X,y = loadnhanes._load()\n",
    "\n",
    "# clean up a bit\n",
    "for c in X.columns:\n",
    "    if c.endswith(\"_isBlank\"):\n",
    "        del X[c]\n",
    "X[\"bmi\"] = 10000 * X[\"weight\"].values.copy() / (X[\"height\"].values.copy() * X[\"height\"].values.copy())\n",
    "del X[\"weight\"]\n",
    "del X[\"height\"]\n",
    "del X[\"urine_hematest_isTrace\"] # would have no variance in the strain set\n",
    "del X[\"SGOT_isBlankbutapplicable\"] # would have no variance in the strain set\n",
    "del X[\"calcium_isBlankbutapplicable\"] # would have no variance in the strain set\n",
    "del X[\"uric_acid_isBlankbutapplicable\"] # would only have one true value in the train set\n",
    "del X[\"urine_hematest_isVerylarge\"] # would only have one true value in the train set\n",
    "del X[\"total_bilirubin_isBlankbutapplicable\"] # would only have one true value in the train set\n",
    "del X[\"alkaline_phosphatase_isBlankbutapplicable\"] # would only have one true value in the train set\n",
    "del X[\"hemoglobin_isUnacceptable\"] # redundant with hematocrit_isUnacceptable\n",
    "rows = np.where(np.invert(np.isnan(X[\"systolic_blood_pressure\"]) | np.isnan(X[\"bmi\"])))[0]\n",
    "X = X.iloc[rows,:]\n",
    "y = y[rows]\n",
    "# Convert to binary prediction of mortality\n",
    "y = y > 0\n",
    "\n",
    "name_map = {\n",
    "    \"sex_isFemale\": \"Sex\",\n",
    "    \"age\": \"Age\",\n",
    "    \"systolic_blood_pressure\": \"Systolic blood pressure\",\n",
    "    \"bmi\": \"BMI\",\n",
    "    \"white_blood_cells\": \"White blood cells\", # (mg/dL)\n",
    "    \"sedimentation_rate\": \"Sedimentation rate\",\n",
    "    \"serum_albumin\": \"Blood albumin\",\n",
    "    \"alkaline_phosphatase\": \"Alkaline phosphatase\",\n",
    "    \"cholesterol\": \"Total cholesterol\",\n",
    "    \"physical_activity\": \"Physical activity\",\n",
    "    \"hematocrit\": \"Hematocrit\",\n",
    "    \"uric_acid\": \"Uric acid\",\n",
    "    \"red_blood_cells\": \"Red blood cells\",\n",
    "    \"urine_albumin_isNegative\": \"Albumin present in urine\",\n",
    "    \"serum_protein\": \"Blood protein\"\n",
    "}\n",
    "mapped_feature_names = list(map(lambda x: name_map.get(x, x), X.columns))\n",
    "\n",
    "# split by patient id\n",
    "pids = np.unique(X.index.values)\n",
    "train_pids,test_pids = train_test_split(pids, random_state=0)\n",
    "strain_pids,valid_pids = train_test_split(train_pids, random_state=0)\n",
    "\n",
    "# find the indexes of the samples from the patient ids\n",
    "train_inds = np.where([p in train_pids for p in X.index.values])[0]\n",
    "strain_inds = np.where([p in strain_pids for p in X.index.values])[0]\n",
    "valid_inds = np.where([p in valid_pids for p in X.index.values])[0]\n",
    "test_inds = np.where([p in test_pids for p in X.index.values])[0]\n",
    "\n",
    "# create the split datasets\n",
    "X_train = X.iloc[train_inds,:]\n",
    "X_strain = X.iloc[strain_inds,:]\n",
    "X_valid = X.iloc[valid_inds,:]\n",
    "X_test = X.iloc[test_inds,:]\n",
    "y_train = y[train_inds]\n",
    "y_strain = y[strain_inds]\n",
    "y_valid = y[valid_inds]\n",
    "y_test = y[test_inds]\n",
    "\n",
    "# mean impute for linear and deep models\n",
    "imp = Imputer()\n",
    "imp.fit(X_strain)\n",
    "X_strain_imp = imp.transform(X_strain)\n",
    "X_train_imp = imp.transform(X_train)\n",
    "X_valid_imp = imp.transform(X_valid)\n",
    "X_test_imp = imp.transform(X_test)\n",
    "X_imp = imp.transform(X)\n",
    "\n",
    "# standardize\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_strain_imp)\n",
    "X_strain_imp = scaler.transform(X_strain_imp)\n",
    "X_train_imp = scaler.transform(X_train_imp)\n",
    "X_valid_imp = scaler.transform(X_valid_imp)\n",
    "X_test_imp = scaler.transform(X_test_imp)\n",
    "X_imp = scaler.transform(X_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /homes/gws/hughchen/anaconda2/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /homes/gws/hughchen/anaconda2/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3368: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From /homes/gws/hughchen/anaconda2/envs/py36/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 8023 samples, validate on 2675 samples\n",
      "Epoch 1/50\n",
      "8023/8023 [==============================] - 1s 97us/step - loss: 0.7000 - acc: 0.6256 - val_loss: 0.5885 - val_acc: 0.6856\n",
      "Epoch 2/50\n",
      "8023/8023 [==============================] - 0s 51us/step - loss: 0.6041 - acc: 0.6828 - val_loss: 0.5112 - val_acc: 0.7742\n",
      "Epoch 3/50\n",
      "8023/8023 [==============================] - 0s 51us/step - loss: 0.5573 - acc: 0.7199 - val_loss: 0.4890 - val_acc: 0.7933\n",
      "Epoch 4/50\n",
      "8023/8023 [==============================] - 0s 52us/step - loss: 0.5178 - acc: 0.7626 - val_loss: 0.4710 - val_acc: 0.8082\n",
      "Epoch 5/50\n",
      "8023/8023 [==============================] - 0s 52us/step - loss: 0.4976 - acc: 0.7815 - val_loss: 0.4559 - val_acc: 0.8164\n",
      "Epoch 6/50\n",
      "8023/8023 [==============================] - 0s 52us/step - loss: 0.4831 - acc: 0.7880 - val_loss: 0.4628 - val_acc: 0.8168\n",
      "Epoch 7/50\n",
      "8023/8023 [==============================] - 0s 52us/step - loss: 0.4698 - acc: 0.7930 - val_loss: 0.4580 - val_acc: 0.8209\n",
      "Epoch 8/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.4650 - acc: 0.8009 - val_loss: 0.4621 - val_acc: 0.8198\n",
      "Epoch 9/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.4593 - acc: 0.8034 - val_loss: 0.4621 - val_acc: 0.8273\n",
      "Epoch 10/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4448 - acc: 0.8122 - val_loss: 0.4439 - val_acc: 0.8217\n",
      "Epoch 11/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4437 - acc: 0.8114 - val_loss: 0.4496 - val_acc: 0.8277\n",
      "Epoch 12/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4434 - acc: 0.8133 - val_loss: 0.4594 - val_acc: 0.8295\n",
      "Epoch 13/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4397 - acc: 0.8168 - val_loss: 0.4494 - val_acc: 0.8280\n",
      "Epoch 14/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4350 - acc: 0.8185 - val_loss: 0.4444 - val_acc: 0.8284\n",
      "Epoch 15/50\n",
      "8023/8023 [==============================] - 0s 56us/step - loss: 0.4383 - acc: 0.8175 - val_loss: 0.4374 - val_acc: 0.8295\n",
      "Epoch 16/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4332 - acc: 0.8199 - val_loss: 0.4527 - val_acc: 0.8273\n",
      "Epoch 17/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4258 - acc: 0.8246 - val_loss: 0.4395 - val_acc: 0.8288\n",
      "Epoch 18/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4233 - acc: 0.8234 - val_loss: 0.4460 - val_acc: 0.8284\n",
      "Epoch 19/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4239 - acc: 0.8254 - val_loss: 0.4294 - val_acc: 0.8258\n",
      "Epoch 20/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.4204 - acc: 0.8265 - val_loss: 0.4228 - val_acc: 0.8280\n",
      "Epoch 21/50\n",
      "8023/8023 [==============================] - 0s 53us/step - loss: 0.4219 - acc: 0.8245 - val_loss: 0.4354 - val_acc: 0.8277\n",
      "Epoch 22/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4198 - acc: 0.8287 - val_loss: 0.4353 - val_acc: 0.8280\n",
      "Epoch 23/50\n",
      "8023/8023 [==============================] - 0s 56us/step - loss: 0.4179 - acc: 0.8260 - val_loss: 0.4288 - val_acc: 0.8299\n",
      "Epoch 24/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4195 - acc: 0.8300 - val_loss: 0.4193 - val_acc: 0.8250\n",
      "Epoch 25/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4154 - acc: 0.8277 - val_loss: 0.4218 - val_acc: 0.8265\n",
      "Epoch 26/50\n",
      "8023/8023 [==============================] - 0s 53us/step - loss: 0.4204 - acc: 0.8281 - val_loss: 0.4285 - val_acc: 0.8288\n",
      "Epoch 27/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.4152 - acc: 0.8284 - val_loss: 0.4256 - val_acc: 0.8265\n",
      "Epoch 28/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4151 - acc: 0.8319 - val_loss: 0.4277 - val_acc: 0.8280\n",
      "Epoch 29/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.4151 - acc: 0.8292 - val_loss: 0.4246 - val_acc: 0.8310\n",
      "Epoch 30/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.4105 - acc: 0.8307 - val_loss: 0.4271 - val_acc: 0.8280\n",
      "Epoch 31/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4123 - acc: 0.8335 - val_loss: 0.4269 - val_acc: 0.8254\n",
      "Epoch 32/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4148 - acc: 0.8330 - val_loss: 0.4225 - val_acc: 0.8299\n",
      "Epoch 33/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4071 - acc: 0.8335 - val_loss: 0.4216 - val_acc: 0.8280\n",
      "Epoch 34/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4090 - acc: 0.8357 - val_loss: 0.4183 - val_acc: 0.8269\n",
      "Epoch 35/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.4081 - acc: 0.8365 - val_loss: 0.4204 - val_acc: 0.8292\n",
      "Epoch 36/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.4090 - acc: 0.8363 - val_loss: 0.4202 - val_acc: 0.8299\n",
      "Epoch 37/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.4018 - acc: 0.8353 - val_loss: 0.4193 - val_acc: 0.8292\n",
      "Epoch 38/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.4085 - acc: 0.8342 - val_loss: 0.4193 - val_acc: 0.8303\n",
      "Epoch 39/50\n",
      "8023/8023 [==============================] - 0s 53us/step - loss: 0.4051 - acc: 0.8346 - val_loss: 0.4176 - val_acc: 0.8250\n",
      "Epoch 40/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.4022 - acc: 0.8353 - val_loss: 0.4172 - val_acc: 0.8269\n",
      "Epoch 41/50\n",
      "8023/8023 [==============================] - 0s 56us/step - loss: 0.4025 - acc: 0.8401 - val_loss: 0.4197 - val_acc: 0.8295\n",
      "Epoch 42/50\n",
      "8023/8023 [==============================] - 0s 56us/step - loss: 0.3987 - acc: 0.8334 - val_loss: 0.4197 - val_acc: 0.8280\n",
      "Epoch 43/50\n",
      "8023/8023 [==============================] - 0s 56us/step - loss: 0.3992 - acc: 0.8402 - val_loss: 0.4220 - val_acc: 0.8269\n",
      "Epoch 44/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.4004 - acc: 0.8375 - val_loss: 0.4203 - val_acc: 0.8262\n",
      "Epoch 45/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.3999 - acc: 0.8353 - val_loss: 0.4226 - val_acc: 0.8239\n",
      "Epoch 46/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.3990 - acc: 0.8382 - val_loss: 0.4165 - val_acc: 0.8265\n",
      "Epoch 47/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.4027 - acc: 0.8376 - val_loss: 0.4177 - val_acc: 0.8262\n",
      "Epoch 48/50\n",
      "8023/8023 [==============================] - 0s 52us/step - loss: 0.3991 - acc: 0.8371 - val_loss: 0.4194 - val_acc: 0.8269\n",
      "Epoch 49/50\n",
      "8023/8023 [==============================] - 0s 54us/step - loss: 0.3928 - acc: 0.8403 - val_loss: 0.4194 - val_acc: 0.8277\n",
      "Epoch 50/50\n",
      "8023/8023 [==============================] - 0s 55us/step - loss: 0.3971 - acc: 0.8370 - val_loss: 0.4198 - val_acc: 0.8273\n",
      "3566/3566 [==============================] - 0s 18us/step\n",
      "Test loss: 0.41299811801212394, Test acc: 0.8286595621004709\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(100, input_dim=X_strain_imp.shape[1], activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_strain_imp, y_strain, epochs=50, batch_size=128,\n",
    "                    validation_data=(X_valid_imp, y_valid))\n",
    "score = model.evaluate(X_test_imp, y_test, batch_size=128)\n",
    "print(\"Test loss: {}, Test acc: {}\".format(score[0],score[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = 6, 2\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Average Baseline***\n"
     ]
    }
   ],
   "source": [
    "import shap\n",
    "stepsize = 1000; n = X_strain_imp.shape[0]\n",
    "print(\"***Average Baseline***\")\n",
    "background = X_strain_imp[np.random.choice(n,stepsize,replace=False)]\n",
    "explainer = shap.DeepExplainer(model, background)\n",
    "sv = explainer.shap_values(X_strain_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.24785495,  0.18079779, -0.1731388 , ...,  0.1680592 ,\n",
       "       -0.14733501, -0.25653276], dtype=float32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(model.predict(X_strain_imp)-model.predict(background).mean())[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "tuple index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-36cb24e72f04>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda2/envs/py36/lib/python3.6/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_mean\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0mis_float16_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m     \u001b[0mrcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_count_reduce_items\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m     \u001b[0;31m# Make this warning show up first\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mrcount\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/py36/lib/python3.6/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_count_reduce_items\u001b[0;34m(arr, axis)\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m         \u001b[0mitems\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mitems\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
     ]
    }
   ],
   "source": [
    "sv[0][0].mean(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x280.8 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(sv[0], features=X_strain_imp, feature_names=list(X.columns.values), max_display=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Average Baseline***\n"
     ]
    }
   ],
   "source": [
    "import shap\n",
    "stepsize = 1000; n = X_strain_imp.shape[0]\n",
    "print(\"***Average Baseline***\")\n",
    "background = X_strain_imp[np.random.choice(n,stepsize,replace=False)]\n",
    "explainer = shap.GradientExplainer(model, background)\n",
    "sv = explainer.shap_values([X_strain_imp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.26146537,  0.16718736, -0.18674922, ...,  0.15444878,\n",
       "       -0.16094543, -0.27014318], dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(model.predict(X_strain_imp)-model.predict(background).mean())[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.0035743 ,  0.00266225, -0.00256349, ...,  0.00165983,\n",
       "       -0.00235164, -0.00347093])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sv[0][0].mean(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Old with old background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "sX = X.iloc[strain_inds,:]\n",
    "X_strain_imp_subset = X_strain_imp[sX.age > 60] # Older subjects\n",
    "foreground_ind = np.random.choice(X_strain_imp_subset.shape[0],1)[0]\n",
    "foreground_ind = 2453\n",
    "foreground = X_strain_imp_subset[foreground_ind:foreground_ind+1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex_isFemale                                 False\n",
       "age                                             67\n",
       "physical_activity                                3\n",
       "serum_albumin                                  NaN\n",
       "alkaline_phosphatase                            47\n",
       "alkaline_phosphatase_isUnacceptable          False\n",
       "alkaline_phosphatase_isTestnotdone           False\n",
       "SGOT                                          2.06\n",
       "SGOT_isUnacceptable                          False\n",
       "SGOT_isTestnotdone                           False\n",
       "BUN                                             13\n",
       "BUN_isUnacceptable                           False\n",
       "BUN_isTestnotdone                            False\n",
       "calcium                                          9\n",
       "calcium_isUnacceptable                       False\n",
       "calcium_isTestnotdone                        False\n",
       "creatinine                                       1\n",
       "creatinine_isUnacceptable                    False\n",
       "creatinine_isTestnotdone                     False\n",
       "potassium                                      3.7\n",
       "potassium_isUnacceptable                     False\n",
       "sodium                                         144\n",
       "sodium_isUnacceptable                        False\n",
       "total_bilirubin                                0.3\n",
       "total_bilirubin_isUnacceptable               False\n",
       "total_bilirubin_isTestnotdone                False\n",
       "serum_protein                                  NaN\n",
       "red_blood_cells                               5.38\n",
       "red_blood_cells_isUnacceptable               False\n",
       "red_blood_cells_isBlankbutapplicable         False\n",
       "                                            ...   \n",
       "cholesterol_isMissing                        False\n",
       "urine_albumin_isNegative                      True\n",
       "urine_albumin_is>=30                         False\n",
       "urine_albumin_is>=100                        False\n",
       "urine_albumin_is>=300                        False\n",
       "urine_albumin_is>=1000                       False\n",
       "urine_albumin_isTrace                        False\n",
       "urine_albumin_isBlankbutapplicable           False\n",
       "urine_glucose_isNegative                      True\n",
       "urine_glucose_isLight                        False\n",
       "urine_glucose_isMedium                       False\n",
       "urine_glucose_isDark                         False\n",
       "urine_glucose_isVerydark                     False\n",
       "urine_glucose_isTrace                        False\n",
       "urine_glucose_isBlankbutapplicable           False\n",
       "urine_pH                                         5\n",
       "urine_pH_isBlankbutapplicable                False\n",
       "urine_hematest_isNegative                     True\n",
       "urine_hematest_isSmall                       False\n",
       "urine_hematest_isModerate                    False\n",
       "urine_hematest_isLarge                       False\n",
       "urine_hematest_isBlankbutapplicable          False\n",
       "sedimentation_rate                             NaN\n",
       "sedimentation_rate_isBlankbutapplicable      False\n",
       "uric_acid                                      5.8\n",
       "uric_acid_isUnacceptable                     False\n",
       "uric_acid_isTestnotdone                      False\n",
       "systolic_blood_pressure                        150\n",
       "pulse_pressure                                  70\n",
       "bmi                                        23.2281\n",
       "Name: 23988, Length: 79, dtype: object"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_sX = sX[sX.age > 60]\n",
    "old_sX.iloc[foreground_ind,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Average Baseline***\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stepsize = 1000\n",
    "print(\"***Average Baseline***\")\n",
    "# n = X_strain_imp_subset.shape[0]\n",
    "# background = X_strain_imp_subset[np.random.choice(n,stepsize,replace=False)]\n",
    "n = X_strain_imp_subset[X_strain_imp_subset[:,0]<0].shape[0]\n",
    "background = X_strain_imp_subset[X_strain_imp_subset[:,0]<0][np.random.choice(n,stepsize,replace=False)]\n",
    "explainer = shap.DeepExplainer(model, background)\n",
    "sv = explainer.shap_values(foreground)\n",
    "# shap.initjs()\n",
    "\n",
    "fig = shap.force_plot(explainer.expected_value, sv[0], features=list(X.columns.values),\n",
    "                      matplotlib=True,show=False)\n",
    "fig.savefig(\"fig/individual{}_oldmalebackground.pdf\".format(foreground_ind))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79640734"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(background).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Average Baseline***\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stepsize = 1000; n = X_strain_imp.shape[0]\n",
    "print(\"***Average Baseline***\")\n",
    "background = X_strain_imp[np.random.choice(n,stepsize,replace=False)]\n",
    "explainer = shap.DeepExplainer(model, background)\n",
    "sv = explainer.shap_values(foreground)\n",
    "# shap.initjs()\n",
    "fig = shap.force_plot(explainer.expected_value, sv[0], features=list(X.columns.values),\n",
    "                      matplotlib=True,show=False)\n",
    "fig.savefig(\"fig/individual{}_allbackground.pdf\".format(foreground_ind))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3409441"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(background).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Old with all background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_strain_imp_f = X_strain_imp[X.iloc[strain_inds,:][\"age\"] > 60] # Older subjects\n",
    "\n",
    "stepsize = 1000; n = X_strain_imp_f.shape[0]\n",
    "print(\"***Average Baseline***\")\n",
    "background = X_strain_imp[np.random.choice(n,stepsize,replace=False)]\n",
    "explainer = shap.DeepExplainer(model, background)\n",
    "sv = explainer.shap_values(X_strain_imp_f)\n",
    "shap.summary_plot(sv[0], features=X_strain_imp_f, feature_names=list(X.columns.values), max_display=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
